ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness
This addresses the bottleneck of human supervision in rover missions, enabling longer autonomous operations on the Moon, though it is incremental as it builds on existing localization methods.
The paper tackles the problem of autonomous global localization for lunar rovers in darkness, presenting ShadowNav, which uses crater edges as landmarks and particle filtering to match detected craters with an offboard map, demonstrating efficacy in simulation and field tests.
The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies. Currently, most on-going rover missions utilize ground-in-the-loop interventions to manually correct for drift in the pose estimate and this human supervision bottlenecks the distance over which rovers can operate autonomously and carry out scientific measurements. In this paper, we present ShadowNav, an autonomous approach for global localization on the Moon with an emphasis on driving in darkness and at nighttime. Our approach uses the leading edge of Lunar craters as landmarks and a particle filtering approach is used to associate detected craters with known ones on an offboard map. We discuss the key design decisions in developing the ShadowNav framework for use with a Lunar rover concept equipped with a stereo camera and an external illumination source. Finally, we demonstrate the efficacy of our proposed approach in both a Lunar simulation environment and on data collected during a field test at Cinder Lakes, Arizona.